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A systematic review of eHealth
systems in developing countries and
practical examples
Hamish SF Fraser MBChB, MRCP, MSc
Director of Informatics and Telemedicine,
Partners In Health
Assistant Professor,
Division of Global Health Equity, Brigham and Womens
Hospital and Harvard Medical School
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Summary
• Motivation for EMR systems in developing
countries
• Systematic review of Global eHealth
• Key studies addressing questions in
delivery of care for HIV and MDR-TB
• Some lessons learned and next steps
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Partners In Health Model of Care
• Access to health care for all people
• Creation of long-term development by partnering
with local people and communities
• Use of community health workers to grow a local
and sustainable work force
• Addressing the effects of poverty including poor
nutrition, water, and housing
• Drawing on the resources of the world’s elite
medical and academic institutions and on the
lived experience of the world’s poorest and
sickest communities
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Directly observed therapy in Haiti
PIH photo
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Status of Global eHealth
• Rapid development over the last 3 years
– Bellagio meeting on e-Health in July 2008
• Driven by the coincidence of:
– need for better Global Health Delivery
– increased resources for health system
strengthening such as the Global Fund, PEPFAR
– more effective, robust, low-cost technologies
– massive growth of mobile phone use and
“mHealth”
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Systematic review of
evaluation studies
Blaya, Fraser, Holt, Health Affairs 2010, 29;2: 244-251
• Surveyed 2043 articles and reports • Used 45 in final analysis• Completed summer 2009
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Summary of the Key Studies
eHealth Category Qualitative Quantitative
Descriptive
Studies
Controlled
Studies
Electronic Health Record (EHR) 5 1 5
Laboratory Information Management Systems (LIMS) 0 1 2
Pharmacy Information Systems 4 2 3
Patient Registration or Scheduling Systems 1 0 2
Monitoring, Evaluation and Patient Tracking Systems 0 2 4
Clinical Decision Support Systems (CDSS) 1 0 3
Patient Reminder Systems 0 1 3
Research or Data Collection Systems 5 1 11
TOTAL 15 8 32
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Findings of the Review
Key functions supported by “initial” evidence:
• Tracking patients through treatment initiation, monitoring adherence, and detecting those at risk for loss to follow-up
• Decreasing time to create administrative reports
• Tools to label or register samples and patients
• Collection of clinical or research data using PDAs
• Reduction in errors in laboratory and medication data
• Reminding patients of health care actions
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Ongoing reviews
• Role of eHealth and mHealth in HIV care
• Review of broader Global eHealth
literature
• Small number of more rigorous studies
have been published in last two years
• Growing interest in mHealth
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Building the evidence base for
decision makers
• How do we answer the question “why
should we invest in eHealth rather than
medical staff, clinics, drugs or training?”
• When does eHealth become important or
essential rather than an option?
• What are the best ways to deliver, support
and sustain eHealth?
• What is not useful or not ready?
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Some examples Supporting HIV
and MDR-TB care
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Original challenge:
Can care for HIV and MDR-TB be delivered:
1. In settings with limited or absent infrastructure?
2. To thousands or tens of thousands of patients?
3. Over long periods of time?
4. With outcomes equivalent to treatment in the developed world?
5. At a “manageable” cost?
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Key processes in HIV care
Case finding and VCT
Registration in pre-ART care
Monitoring clinical & lab status
Starting on ART
Drug supply management
Ensuring adherence to Rx
Monitoring side effects and
opportunistic infections
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Key processes in HIV care
Case finding and VCT
Registration in pre-ART care
Monitoring clinical & lab status
Starting on ART
Drug supply management
Ensuring adherence to Rx
Monitoring side effects and
opportunistic infections
Patient
numbers?
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Key processes in HIV care
Case finding and VCT
Registration in pre-ART care
Monitoring clinical & lab status
Starting on ART
Drug supply management
Ensuring adherence to Rx
Monitoring side effects and
opportunistic infections
Home based care,
AMPATH mHealth
Registry/EMR
CD4 alert and tracking
Registry, EMR system
Inventory / dispensary
and shipping systems
CHW, mHealth/SMS
Paper flowsheets,
EMR alerts, CHW
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OpenMRS:
a modular, open source, EMR platform
• Developed as a collaboration of PIH, the Regenstrief
Institute and South African MRC
• Uses concept dictionary for data storage
• Modular design simplifies adding new functions and linking
to other systems
• Released with open source license (April 2007)
• Core of paid programmers with growing community support
• Clinical use in over 40 developing countries
• Secure logins and auditing of access and data changes
• www.openmrs.org
Partners In Health Regenstrief InstituteMedical reseach council SA
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OpenMRS at PIH
sites in Rwanda
• Currently used for 24 PIH –
supported MOH health centers
• HIV, TB, primary care and heart failure care
• 20,000 patients tracked (approx)
• Rwandan data officers, data managers, and
programmers
• Many sites have their own server and maintain a
synchronized copy of the entire database
• Using laptop servers and cellular GPRS network
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Rwinkwavu
Infectious
Disease clinic
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Evaluating access to CD4 counts
• We evaluated whether the ID physicians
had access to the latest CD4 count for
their patients in Rwinkwavu, Rwanda
• The physicians record their belief of the
correct CD4 on the follow-up form based
on paper lab result forms
• We checked if CD4 was current before
and after a new lab component was added
to the EMR to ensure up to date results
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Clinical Alerts (Rwinkwavu, Rwanda)
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Results – Access to CD4 counts
• The proportion of CD4 counts conducted
within the past 60 days but unknown to the
clinician at the time of consultation was:
• 24.7% in the pre-intervention period
• 16.7% in the post intervention period
• 32.4% reduction in CD4 loss (p=.002)
• We are now extending direct clinician
access to the EMR
Amoroso et al, Stud Health Technol Inform. 2010;160:337-41
:
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Physician looking up ARV patients
Photo Rockefeller Foundation
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Impact of OpenMRS patient
summaries at AMPATH
• The OpenMRS EMR system at AMPATH in Western
Kenya was used to generate printed patient summaries
including reminders for ordering repeat CD4 counts
• The computerized reminder system identified 717
encounters (21%) with overdue CD4 counts
• In the intervention clinic with computer-generated
reminders, CD4 order rates were significantly higher
compared to the control clinic:
53% vs 38%, OR =1.80, CI 1.34 to 2.42, p<0.0001
• Order rates in intervention clinic were even higher (63%)
in cases where the summary was actually printed.
Were MC, et al. J Am Med Inform Assoc (2011).doi:10.1136/ jamia.2010.005520
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HIV Treatment Adherence
Reminders (Lester)
• RCT of weekly SMS reminders for ART adherence
in Kenya with follow-up by phone if no response
• Outcomes:
– self-reported ART adherence (>95% of prescribed doses
in the past 30 days at both 6 and 12 month follow-up
visits)
– Plasma HIV-1 viral RNA load suppression
Lester et al, Lancet Vol 376 November 27, 2010
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SMS reminders: outcome
• Adherence to ART was reported in 168 of 273
(61.5%) patients receiving the SMS intervention
compared with 132 of 265 (49.8%) in the control
group ( [RR] 0·81, 95% CI 0·69–0·94; p=0·006)
• Suppressed viral loads were reported in 156 of
273 patients in the SMS group and 128 of 265 in
the control group,
• (RR for virologic failure 0·84, 95% CI 0·71–
0·99; p=0·04)
• Loss to follow up rate 6% intervention, 11%
control P=0.094
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HIV Treatment Adherence
Reminders (Pop-Eleches)
• RCT of four SMS reminder interventions
• 431 adults randomized to control of one of 4
interventions
• Short or long SMS reminders daily or weekly
• MEMS pharmacy monitor used for adherence
• Outcome: adherence >90% for 48 weeks in 53%
with weekly reminder, and 40% in controls
• Short and weekly SMS were much better
• Loss to follow-up rates: 10 – 22% no change
Pop-Eleches et al, AIDS 2011, 25: 825 - 834
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Other important studies
• SMS for Life: a pilot project to improve anti-
malarial drug supply management in rural
Tanzania using standard technology.
(Barrington et al)
• Evaluation of computerized health management
Information system for primary health care in
rural India (Krishnan et al)
• The effect of mobile phone text-message
reminders on Kenyan health workers’ adherence
to malaria treatment guidelines: a cluster
randomised trial. (Zurovac, et al Lancet 2011)
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eChasqui, Lima
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Example: MDR-TB in Lima, Peru
• Highest incidence of TB in South America
• 40,000 patients treated with DOTS per year
• > 3% have MDR-TB
DOTS = directly observed therapy
short course
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eChasqui System, Peru
Mean time6 months
Transportation - Mototaxi
Blaya J et al, Int J Tuberc Lung Dis. 2010 Aug;14(8):1009-15
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JA Blaya
Outcomes of Interest
• Turn-around-times (TAT)
• Proportion of Late DST Results (Lab TAT > 60 days)
• Number of communication errors
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eChasqui Results (delays)
• Intervention health centers took significantly less
time to receive:
- DST results (median 11 vs. 17 days, p<0.001)
- culture results (5 vs. 8 days, p<0.001)
- 47% fewer DSTs took over 60 days to arrive
(p=0.12).
• No change in time to start or modify treatment
• Patients in intervention health centers had a 20%
reduction in time to culture conversion (p=0.047).
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eChasqui results (errors)
• Results reported in intervention HCs compared
to control HCs had respectively:
• 82% less errors for drug susceptibility tests
(2.1% vs. 11.9%, P < 0.001, OR 0.17, 95% CI
0.09–0.31)
• 87% less errors for cultures (2.0% vs. 15.1%, P
<0.001, OR 0.13, 95%CI 0.07–0.24),
• Preventing missing results through online
viewing accounted for at least 72% of all errors.
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JA Blaya 34/21
DST Lab TATRandomized Trial
ResultsIntervention HCs havesignificantly lower
1. DST Lab TAT (p<0.001) - 11 vs 17 days (median)
17.7%
7.9%
60
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Some success and failure factors
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Impact of health care investments
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Impact
Vaccines
Medical training
New hospital
Supply chain
CHWs
Nurse training
Teaching Hospital
Operating room
Investment
Mobile clinics
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Impact of Ehealth Investment?
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Impact
Power,
Leadership
Software,Staffing,
Hardware,
Training,
Investment
Networking,
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Impact of Ehealth Investment?
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Impact
Power,
Leadership
Software,Staffing,
Hardware,
Training,
Investment
Networking,
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Impact of Ehealth Investment?
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Impact
Power,
Leadership
Software,Staffing,
Hardware,
Training,
Investment
Networking,
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Impact of Ehealth Investment?
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Impact
Power,
Leadership
Software,Staffing,
Hardware,
Training,
Investment
Networking,
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MOH Village
CHWDist Clinic
The importance of local data use
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MOH District Clinic
The importance of local data use
Village
CHW
Avoid systems that just suck!
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Call to Action on Global eHealth Evaluation
Consensus Statement of the WHO Global eHealth
Evaluation Meeting,
Bellagio, September 2011
“To improve health and reduce health inequalities, rigorous
evaluation of eHealth is necessary to generate evidence
and promote the appropriate integration and use of
technologies.”
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Bellagio Call to Action
• Develop repository of tools, studies,
frameworks, teaching materials
• Refine frameworks – GEP-HI, PRISM,
KDS, etc.
• Create training course in developing
countries
• Advocate to funders require evaluation in
eHealth projects
• Follow up meeting in SF last week
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Observations
• Large investment in eHealth to date -
$Billions!!
• Particular need to monitor day to day
performance and activities:
– down time, forms entered, usage, data quality
and completeness
• Measuring impact of eHealth, what are the
alternatives ans control groups?
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Conclusion
• The evidence base is improving…slowly
• There is still much to do - even the most
rigorous studies have many unknowns
• Important questions of how role of e/mHealth
differs in resource poor environments
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Collaborators and Funders • Partners In Health
• Regenstrief institute
• Medical Research Council, South Africa
• World Health Organization
• US Centers for Disease Control
• Brigham and Women hospital
• Harvard Medical School
• University of KwaZulu-Natal
• Millennium Villages Project
• International Development Research
Centre, Ottawa
• Rockefeller Foundation
• Google Inc
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